Land fragmentation in southern Ontario: A tragedy of the spatial anticommons
Bibliographic record
Abstract
Competition between agricultural operations, urban transplants, and ecological interests is changing the nature of property rights and land use in rural Ontario. In a region with valuable ecosystems and climate, soil, and location traditionally well-suited for crop and livestock production, plot sizes are decreasing as land is subdivided and allocated to non-agricultural residential use. Although this practice can increase property value for farmers, Michael Heller’s spatial anticommons may also be observed, such that “each owner receives a core bundle of rights, but in too small a space for the most efficient use” [2]. The purpose of this paper is to introduce a new application of Heller’s anticommons theory, examining how the increasingly patchwork-like distribution of rural land parcels can be expected to affect farm and ecosystem productivity. Ultimately, deadweight loss occurs because neither agricultural nor ecological economies of scale can be recognized on plots that are too small for efficient use. Using rural planning reports and habitat ecology studies, trends in the fragmentation process are described and compared to the aims of provincial land-use policy, including the Provincial Policy Statement, the Greenbelt Act, and the Places to Grow Act. While the goals of farmers and conservationists may at times seem discrete or incompatible, the anticommons framework may be used to identify shared challenges. Thus the two parties might consider how collective action could be used to overcome the difficulties of reuniting subdivided tracts of land.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".